Based on the established values of the Arrhenius model parameters, predictions are made for the evolution of viscosity of the resin during isothermal curing with different temperatures, and different amounts of catalyst.
Design of a cost-effective data collection scheme, i.e., data-worth analysis, requires quantifying model parameter, prediction, and both current and potential data uncertainties.
In order to evaluate how well mechanistic models could be built upon inferred biological networks, we tested the accuracy of model parameter predictions and missing link identification.
We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability.
We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions.
Finally, the methods have a theoretical foundation that automatically supports rigorous computing of uncertainties, i.e., confidence bands on the model parameters, and prediction bands on the expected health outcomes.
A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread.
The Bayesian approach allowed credible and prediction intervals to be calculated; thus, informed statistical inferences about model parameters and predictions of future mortality, in particular the scale and timing of the peak in deaths, could be made.
This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness.
In recent decades, Bayesian data assimilation methods have been widely used in hydrology to assess uncertain model parameters and predictions.
